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Using Jupyter/IPython Notebooks in Azure Machine Learning

In this webinar, we'll cover what Jupyter notebooks are, the integration with Azure Machine Learning Studio, and operationalization of code to run on the Azure Machine Learning backend.

Jupyter notebooks (formerly IPython) provide a highly productive canvas for data scientists and developers to explore ideas. At its heart, Jupyter is a multi-lingual REPL (read eval print loop), where you can enter code and get a response. The response can be program output, a graph, etc. The notebook is comprised of interspersed code and markdown text for documentation purposes. For examples of Notebooks, take a look at http://nbviewer.org.
We're delighted to announce the availability of Jupyter notebooks as a service on Azure Machine Learning. It is integrated with the Azure Machine Learning Studio, which means you can explore your datasets, write code, and build models conveniently from Notebook. Want to use Pandas or Seaborn to check out a data set, visualize it, slice/dice it and store it back? Simple: just click the data set in the Studio and select "Open in Notebook.” Best of all, there is no installation required. You can use Jupyter notebooks with Machine Learning from any modern browser on any operating system.